Evaluating Federated Learning for At-Risk Student Prediction: A Comparative Analysis of Model Complexity and Data Balancing

The introduction of SpectralKrum presents a novel defense mechanism against Byzantine attacks in Federated Learning (FL), addressing vulnerabilities where malicious clients can disrupt the training process by submitting corrupted updates. This method combines spectral subspace estimation with geometric neighbor-based selection to enhance the robustness of model training across heterogeneous client data distributions.

A novel module-wise federated learning framework has been proposed to enhance grasp pose detection (GPD) in cluttered environments, addressing the challenges of data privacy and communication overhead associated with large models. This framework identifies slower-converging modules and allocates additional communication resources during training, thereby improving efficiency for resource-constrained robots.